IDEAS home Printed from https://ideas.repec.org/a/hin/jnddns/916382.html
   My bibliography  Save this article

Locally Linear Discriminate Embedding for Face Recognition

Author

Listed:
  • Eimad E. Abusham
  • E. K. Wong

Abstract

A novel method based on the local nonlinear mapping is presented in this research. The method is called Locally Linear Discriminate Embedding (LLDE). LLDE preserves a local linear structure of a high-dimensional space and obtains a compact data representation as accurately as possible in embedding space (low dimensional) before recognition. For computational simplicity and fast processing, Radial Basis Function (RBF) classifier is integrated with the LLDE. RBF classifier is carried out onto low-dimensional embedding with reference to the variance of the data. To validate the proposed method, CMU-PIE database has been used and experiments conducted in this research revealed the efficiency of the proposed methods in face recognition, as compared to the linear and non-linear approaches.

Suggested Citation

  • Eimad E. Abusham & E. K. Wong, 2009. "Locally Linear Discriminate Embedding for Face Recognition," Discrete Dynamics in Nature and Society, Hindawi, vol. 2009, pages 1-8, January.
    • Handle: RePEc:hin:jnddns:916382
    DOI: 10.1155/2009/916382
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/DDNS/2009/916382.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/DDNS/2009/916382.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2009/916382?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:jnddns:916382. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.